Regional differences in the prevalence of known Type 2 diabetes mellitus in 45–74 years old individuals: Results from six population‐based studies in Germany (DIAB‐CORE Consortium)
Bibliographic record
Abstract
AIM: In Germany, regional data on the prevalence of Type 2 diabetes mellitus are lacking for health-care planning and detection of risk factors associated with this disease. We analysed regional variations in the prevalence of Type 2 diabetes and treatment with antidiabetic agents. METHODS: Data of subjects aged 45-74 years from five regional population-based studies and one nationwide study conducted between 1997 and 2006 were analysed. Information on self-reported diabetes, treatment, and diagnosis of diabetes were compared. Type 2 diabetes prevalence estimates (95% confidence interval) from regional studies were directly standardized to the German population (31 December 2007). RESULTS: Of the 11,688 participants of the regional studies, 1008 had known Type 2 diabetes, corresponding to a prevalence of 8.6% (8.1-9.1%). For the nationwide study, a prevalence of 8.2% (7.3-9.2%) was estimated. Prevalence was higher in men (9.7%; 8.9-10.4%) than in women (7.6%; 6.9-8.3%). The regional standardized prevalence was highest in the east with 12.0% (10.3-13.7%) and lowest in the south with 5.8% (4.9-6.7%). Among persons with Type 2 diabetes, treatment with oral antidiabetic agents was more frequently reported in the south (56.9%) and less in the northeast (46.0%), whereas treatment with insulin alone was more frequently reported in the northeast (21.6%) than in the south (16.4%). CONCLUSION: The prevalence of known Type 2 diabetes showed a southwest-to-northeast gradient within Germany, which is in accord with regional differences in the distribution of risk factors for Type 2 diabetes. Furthermore, the treatment with antidiabetic agents showed regional differences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".